Continuous Bayesian networks for the estimation of species richness

We propose a new methodology based on continuous Bayesian networks for assessing species richness. Specifically, we applied a restricted structure Bayesian network, known as tree augmented naive Bayes (TAN), regarding a set of environmental continuous predictors. First, we analysed the relationships between the response variable (called the terrestrial vertebrate species richness) and a set of environmental predictors. Second, the learnt model was used to estimate the species richness in Andalusia (Spain) and the results were depicted on a map. In addition to this, the TAN model was compared to three other methods commonly used for regression in terms of their root mean squared error. The experimental results showed that the TAN model not only was competitive from the point of view of accuracy but also managed to deal with the species richness–environment relationship, which is complex from the ecological point of view. The results highlight that landscape heterogeneity, topographical and social variables had a direct relationship with species richness while climatic variables showed more complicated relationships with the response. © 2015, Springer-Verlag Berlin Heidelberg.